- We are looking for an Applied Scientist to join a dynamic and innovative AI platform team that is pushing the boundaries of what's possible in sales execution. If you are passionate about applying cutting-edge research in knowledge graphs and reasoning systems to real-world problems at scale, this is an exceptional opportunity to shape a core piece of Outreach's AI architecture from the ground up.
- Our team is building a per-tenant contextual knowledge graph that captures the full complexity of each customer's sales environment: accounts, deals, contacts, rep behaviors, competitive landscape, and the signals buried in calls, emails, and CRM activity. This graph powers contextual reasoning across the platform, driving next-best-action recommendations, deal risk signals, coaching suggestions, and competitive intelligence. In this pivotal role, you will design the underlying representations, extraction pipelines, and reasoning layers that make this possible, working closely with cross-functional engineering and product teams to deliver innovative, scalable, and reliable AI capabilities with direct impact on revenue outcomes.
Your Daily Adventures Will Include:
- Knowledge Graph Design & Construction: Architect and evolve per-tenant knowledge graph schemas, including entity resolution, temporal modeling, and ontology design tailored to sales execution domains.
- Information Extraction: Architect NLP pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection.
- Contextual Reasoning & Recommendation: Design reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, and competitive intelligence surfaces.
- Representation Learning: Design and train graph-based models (GNNs, relational embeddings, link prediction) over heterogeneous, multi-relational graph structures to support downstream reasoning and retrieval tasks. Diagnose and address embedding quality issues including cold-start entities, and temporal drift.
- Domain Modeling: Formalize sales execution concepts such as deal stages, buyer engagement patterns, rep behaviors, and account health, into structured representations that ground the platform's AI capabilities. Extract ontology structure. Lead ontology versioning and migration.
- Cross-functional Collaboration: Partner with engineering, product, and data teams to bring models from prototype to production, ensuring reliability and measurable impact at scale.
Our Vision of You:
- PhD in a relevant field such as Computer Science, NLP, Machine Learning, or a related discipline with a focus on knowledge representation and reasoning, information extraction and relationship extraction, graph neural networks, recommendation systems, or conversation AI and dialogue systems.
- Strong engineering fundamentals. You can write production-quality code, not just prototype notebooks. Proficiency in Python; and graph databases or query languages (e.g., Neo4j, SPARQL, Cypher) is required.
- Comfort with ambiguity. You can take a vague product goal and decompose it into concrete technical problems. You don't need a fully scoped spec to start making progress.
- A track record of building things: whether that's research prototypes that went beyond the paper, open-source contributions, or side projects that required real systems thinking. You understand the gap between a research prototype and a reliable production system, such as monitoring, data drift, latency, and operational excellence.
- Strong Ownership: Take end-to-end responsibility for research and model development initiatives, from problem formulation and data analysis through experimentation, production deployment, and ongoing performance monitoring, driving outcomes with minimal oversight.
- Strong communication skills with the ability to translate research concepts into product impact for cross-functional audiences.
- Experience mentoring or leading technical work. You've helped junior team members grow and have driven cross-team technical decisions.
Nice to Have:
- 2+ years of hands-on experience applying knowledge graphs or graph-based learning methods to real-world data in a production setting.
- Strong fundamentals in at least two of: knowledge graph construction, information extraction, graph neural networks, or recommender systems.
- Experience working with large-scale unstructured text data (conversational transcripts, email, or similar)
- Experience with probabilistic graphical models, conversational AI, or sales/revenue domain data
- Published research at top-tier venues
Why Join Us?
- Greenfield Architecture: Shape the design of a core AI system from the ground up, with the latitude to make foundational technical decisions that define the platform.
- Depth That Matters: This role genuinely requires PhD-level thinking; you will tackle problems in entity resolution, temporal reasoning, and graph learning that demand it.
- Applied Impact: Work with real production feedback loops and millions of sales interactions, not just benchmarks; see your models change how thousands of teams sell.
- High Leverage, Low Bureaucracy: Join a small, senior team where your contributions are visible, your ideas ship fast, and you have direct access to leadership.
- Career Growth: Opportunity to lead initiatives and mentor engineers.
Skills Required
- PhD in Computer Science, NLP, Machine Learning, or related field focused on knowledge representation and reasoning
- Proficiency in Python and ability to write production-quality code
- Experience with graph databases or query languages (e.g., Neo4j, SPARQL, Cypher)
- Strong engineering fundamentals and production systems understanding (monitoring, data drift, latency, operational excellence)
- Experience translating research into product impact and taking end-to-end ownership from problem formulation to deployment
- Strong communication skills and cross-functional collaboration experience
- Experience mentoring or leading technical work
Outreach Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Outreach and has not been reviewed or approved by Outreach.
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Parental & Family Support — Parental leave is described as unusually generous, including extended leave and distinctive transition support such as a paid night nurse option and food delivery. Family-oriented benefits are repeatedly positioned as a standout part of the overall package.
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Healthcare Strength — Medical, dental, and vision coverage is described as comprehensive, with the employer covering a majority of premiums in many cases. Mental health support and an EAP for confidential counseling are also included as part of the health offering.
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Equity Value & Accessibility — Equity (stock options/RSUs) is commonly included as part of total compensation and is framed as a meaningful component of rewards. For some roles, equity is viewed as a notable source of upside that complements cash compensation.
Outreach Insights
What We Do
Outreach is the number one sales engagement platform. Using advanced machine learning and AI to automate and prioritize customer touchpoints, Outreach dramatically increases sales reps' effectiveness and ability to drive smarter, more insightful engagement with their customers. We're on a mission to make every customer-facing rep wildly productive.
Why Work With Us
We balance explosive growth with unwavering values. We believe in agility, but we don't compromise on high standards or delivering the best quality. Everyone truly wants to do the right thing. At Outreach, you are not only permitted to own your business, but expected to. If you're excited by ownership, you'll fit right in. You will never be bored.
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